Dataset Information

Waist circumference does not improve established cardiovascular disease risk prediction modeling.

ABSTRACT: Despite considerable evidence demonstrating that waist circumference (WC) is independently associated with cardiovascular disease (CVD) and/or all-cause mortality, whether the addition of WC improves risk prediction models is unclear. The objective was to evaluate the improvement in risk prediction with the addition of WC, alone or in combination with BMI, to the Framingham Risk Score (FRS) and a population specific model. 34,377 men and 9,477 women aged 20 to 79 years who completed a baseline examination at the Cooper Clinic (Dallas, TX) during 1977-2003 and enrolled in the Aerobics Center Longitudinal Study (ACLS). WC was measured at the level of the umbilicus and expressed as a continuous variable. Deaths among participants were identified using the National Center for Health Statistics National Death Index. A total of 728 fatal cardiovascular disease (CVD) events occurred over a mean follow-up period of 13.1 ± 7.5 years. WC was significantly higher in CVD decedents (P = .002). The FRS C-statistic for fatal CVD in men was 0.836 (0.816-0.855) and 0.883 (0.851-0.915) in women. The addition of WC did not improve the C-statistic in men (0.831 (0.809-0.853)) or women (0.883 (0.850-0.916)). Similar findings were observed for non-fatal CVD and all-cause mortality, and when WC was added to a population specific model. Upon adding WC, the net-reclassification index was 0.024 with an integrated discrimination improvement of -0.0004. The addition of WC, alone or in combination with BMI, did not substantively improve risk prediction for CVD or all-cause mortality compared to the Framingham Risk Score or a population specific model.

SUBMITTER: Nelms MW

PROVIDER: S-EPMC7531816 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Waist circumference does not improve established cardiovascular disease risk prediction modeling.

Nelms Matthew W MW Day Andrew G AG Sui Xuemei X Blair Steven N SN Ross Robert R

PloS one 20201002 10

Despite considerable evidence demonstrating that waist circumference (WC) is independently associated with cardiovascular disease (CVD) and/or all-cause mortality, whether the addition of WC improves risk prediction models is unclear. The objective was to evaluate the improvement in risk prediction with the addition of WC, alone or in combination with BMI, to the Framingham Risk Score (FRS) and a population specific model. 34,377 men and 9,477 women aged 20 to 79 years who completed a baseline e ...[more]

PMID: 33007042

Similar Datasets

Project description:BackgroundWaist circumference is becoming recognized as a useful predictor of health risks in clinical research. However, clinical datasets tend to lack this measurement and self-reported values tend to be inaccurate. Predicting waist circumference from standard physical features could be a viable method for generating this information when it is missing or mitigating the impact of inaccurate self-reports. This study determined the degree to which the XGBoost advanced machine learning algorithm could build models that predict waist circumference from height, weight, calculated Body Mass Index, age, race/ethnicity and sex, whether they perform better than current models based on linear regression, and the relative importance of each feature in this prediction.MethodsWe trained tree-based models (via XGBoost gradient boosting) and linear models (via regression) to predict waist circumference from height, weight, Body Mass Index, age, race/ethnicity and sex (n = 60,740 participants). We created 10 iterations of each model, each using 90% of the dataset for training and the remaining 10% for testing performance (this group was different for each iteration). We calculated model performance and feature importance as an average across 10 iterations. We then externally validated the ensembled version of the top model.ResultsThe XGBoost model predicted waist circumference with a mean bias ± standard deviation of 0.0 ± 0.04 cm and a root mean squared error of 4.7 ± 0.05 cm, with performance varying slightly by sex and race/ethnicity. The XGBoost model showed varying degrees of improvement over linear regression models. The top 3 predictors were Body Mass Index, weight and race (Asian). External validation found that on average this model overestimated waist circumference by 4.65 cm in the United Kingdom population (mainly due to overprediction in females) and underestimated waist circumference by 1.7 cm in the Chinese population. The respective root mean squared errors were 7.7 cm and 7.1 cm.ConclusionsXGBoost-based models accurately predict waist circumference from standard physical features. Waist circumference prediction using this approach would be valuable for epidemiological research and beyond.

Dataset Information

Waist circumference does not improve established cardiovascular disease risk prediction modeling.

Publications

Waist circumference does not improve established cardiovascular disease risk prediction modeling.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets